Since its introduction 40 years ago, atomic force microscopy (AFM) has transformed nanoscale imaging across the physical and life sciences. In biology, AFM has provided unprecedented insights into nanoscale biomolecular organisation under near-physiological conditions, revealing structures and interactions inaccessible to many conventional approaches. However, biological function is inherently dynamic, and understanding molecular mechanisms requires moving beyond static structural snapshots towards direct observation of processes across multiple spatial and temporal scales. In this review, we discuss emerging approaches that are extending the capabilities of AFM beyond traditional imaging, pushing the limits of temporal resolution, spatial precision, quantitative measurement, and data analysis. We highlight advances in speed that enable direct visualisation of molecular dynamics, approaches for sub-millisecond measurements, methods that overcome conventional resolution limits, and computational approaches that enhance image analysis, automation, and information extraction from complex datasets. Together, these developments are transforming AFM from a technique focused primarily on high-resolution structural imaging into a platform for dynamic, quantitative, and high-throughput investigation of biological systems. Finally, we consider how these advances may shape the next 40 years of AFM, enable autonomous measurements and reveal biological mechanisms across previously inaccessible spatial and temporal scales.

Quantitative analysis of embryonic morphogenesis often requires handling terabyte-scale 4D microscopy datasets, posing major computational and methodological challenges. Such analyses involve complex workflows for image restoration, segmentation, and tracking of thousands of cells in 3D, sometimes assembled from multiple generic tools and custom scripts. In their study 'Deconstructing gastrulation at single-cell resolution,' the authors present an integrated computational framework for cell segmentation, tracking and modelling of cell-cell interactions during Drosophila gastrulation. Their approach combines both generic tools such as ilastik, FIJI, Cellpose, MATLAB code and tools such as ImSAnE to reconstruct whole-embryo, single-cell morphogenetic atlas with unprecedented detail. While the methodology is transparent and well-documented, its implementation relies on multiple software environments and some non-open-source components, limiting full reproducibility and generalisability. Nevertheless, their 'computational framework' exemplifies rigorous data sharing, clear documentation, and methodological transparency aligned with several community guidelines for reproducible image analysis. We discuss this work in the context of reproducibility, replicability, generalisability, transparency, validity, and robustness, emphasising how emerging open-source and popular solutions could improve accessibility and replicability of similar large-scale bioimage analysis workflows in developmental biology.

Recent developments in cancer mechanobiology have highlighted the importance of tumour mechanics as a potential marker of disease progression. Three-dimensional tumour models provide simplified yet physiological relevance exhibiting mechanical heterogeneity. However, comprehensively capturing both bulk and localised biomechanical properties remains challenging. In this study, triple negative breast cancer live spheroids were assessed through non-invasive Brillouin microscopy as well as micro-compression testing which provide maps of microscale mechanical heterogeneity and bulk-scale mechanical assessment, respectively. Together, these approaches form a multi-scale mechanical framework that may improve the interpretation of disease-related mechanical changes and support cancer treatment discovery.

Electron spin resonance (ESR) is one of the most powerful spectroscopic methods for probing systems with unpaired electronic spins. Conventional ESR has usually relied on the detection of large spin ensembles, thereby averaging over local environments and suppressing microscopic information that is central to surface science, molecular magnetism, and quantum nanoscience. Scanning probe microscopy (SPM) changes this situation. By combining magnetic resonance with the spatial resolution and manipulation capabilities of scanning tunnelling microscopy (STM) and atomic force microscopy (AFM), ESR can now be performed on individual atoms and molecules on surfaces. This short perspective discusses three connected developments. First, ESR-STM has enabled all-electrical detection and coherent control of individual spins with atomic precision. Second, ESR-AFM has recently provided a complementary route to single-molecule spin resonance based on force-detected pump-probe protocols. Third, these advances are turning surface spins into atomically engineered quantum systems in which spin Hamiltonians, interactions, sensing architectures, and elementary quantum operations can be designed locally. The resulting field does not merely miniaturise conventional ESR; it transforms magnetic resonance into a local quantum tool for spectroscopy, sensing, and coherent manipulation at the atomic scale.

AFM is not for the faint of heart. It has never been for these 40 years since its invention or for any of the modalities that have emerged. And among them, the initial concept of using AFM to measure cell mechanics was simply brazen. On the one hand, those pioneers hacked an imaging tool optimised for minimal mechanical interaction between probe and sample, and used it to locally indent living cells. On the other hand, they did so to measure stiffness of cells, a feature that no one in the cell biology or biomedicine communities gave relevance to or even credit. Through their total disregard for the status quo, they helped establish two topics that today employ thousands of researchers worldwide: AFM for nanomechanical mapping of soft samples, and the research field of mechanobiology.

In 1922, Léon Brillouin introduced the idea of light scattering from thermal fluctuations, in a paper titled: 'Diffusion de la lumière et des rayons X par un corps transparent et homogéne, influence de l'agitation thermique'. Brillouin's mathematical treatment elegantly unifies principles from thermodynamics, optics and electromagnetic theory (as they were understood at the time) to arrive at a set of equations describing the scattering process, in a form that is still in use today. The effect he describes became known as Brillouin scattering, and was experimentally verified in 1930. It would go on to become the underlying principle of novel sensing and lasing technologies, as well as more recently also Brillouin microscopy. Despite the elegant derivation presented by Brillouin, and its scientific significance, the original French manuscript is rarely studied by the scientific community. With English serving as the lingua franca of our time, doing so is understandably challenging for the broader research community. While the original manuscript presents coherent and well-founded theoretical derivations, following the thought process of the author, from a direct literal translation of the article can be somewhat challenging. The paper uses dated notations, some less common mathematical transformations, and in some cases seemingly unjustified physical simplifications. It also on occasion changes symbols for a quantity half way through, and there are several typographical errors that might confuse readers not intimately familiar with his line of argument. Here we provide an English translation of the original manuscript, with modernized notations and corrections, together with a supplementary text in which we discuss the derivation of each of the equations, along with the assumptions used in obtaining them. Our hope is that by providing the literal translation together with the accompanying discussion, we can transform this document from a blindly cited historical artefact to an accessible pedagogical tool that provides insight into the scientific development and discussions in the 1920's.

Soft X-ray tomography (SXT) is a label-free, high-resolution imaging modality that is optimal for 3D visualisation of whole hydrated cells. Although SXT has been extensively used to study remodelling in more than 130 mammalian cell lines, its application to plants remains largely unexplored. In this study, we present the first approach at full-rotation SXT imaging of spreading earthmoss P. patens, which is a key model organism for studying plant cell architecture and developmental processes. We show that high-resolution, three-dimensional imaging of fully hydrated P. patens protoplasts enable visualisation of organelle morphology and distribution. These proof-of-concept results demonstrate the potential of SXT to capture ultrastructural changes in plant cells, depending on developmental cues, stress responses, and genotype-dependent variation.

Far-red light (FRL) cyanobacteria occupy spectrally filtered habitats where visible wavelengths are depleted but far-red radiation persists. Shark Bay (Western Australia) stromatolitic microbial mats provide such a habitat, containing subsurface cyanobacterial communities in which chlorophyll d/f (Chl d/f)-containing phototrophs coexist with filamentous Halomicronema-like taxa adapted to far-red photosynthesis. In this Journal of Microscopy issue honouring Associate Professor Guy C. Cox, we used FRL-sensitive confocal imaging to visualise far-red cyanobacteria in intact Shark Bay mat samples, and combined microspectroscopy, lifetime microscopy, HPLC pigment analysis and synchrotron FTIR microspectroscopy to examine pigment organisation and FRL acclimation in cultured Halomicronema sp. 'Clifton'. Confocal imaging with near-infrared-sensitive detection revealed abundant far-red fluorescent cyanobacteria within subsurface mat layers and enabled direct visualisation of Chl d/f-containing phototrophs in situ. In cultured Halomicronema, HPLC confirmed that Chl f accumulation was induced by FRL acclimation and absent under white light, validating assignment of the dominant 730-745 nm emission in FRL-grown cells to Chl f-enriched photosystems. Intracellular excitation-emission mapping showed that FRL acclimation did not produce complete pigment segregation, but generated a pronounced intracellular pigment gradient, with Chl f-associated fluorescence enriched in mid-cell regions and phycobiliprotein- and chlorophyll a (Chl a)-associated fluorescence enriched at cell poles. FLIM and phasor analyses showed that this spatial reorganisation was accompanied by altered excited-state behaviour, including strongest lifetime shortening in Chl a-associated channel, additional shortening in the far-red chlorophyll-enriched channel and increased heterogeneity of PC/APC-associated fluorescence. The absence of a distinct Chl f-only lifetime population indicates that Chl f remains functionally integrated within coupled photosynthetic complexes rather than acting as an isolated fluorescent sink. By linking optical stratification and far-red cyanobacterial diversity within Shark Bay microbial mats to pigment organisation and fluorescence dynamics in living Halomicronema cells, this study extends Guy Cox's microscopy-based exploration of photosynthetic microorganisms and demonstrates the power of modern spectral confocal microscopy for investigating photosynthesis at the far-red edge of oxygenic life.

Engineered probiotic biointerfaces are the dynamic boundaries through which modified bacteria interact with their biological surroundings. They can improve bacterial survival, mucosal retention and therapeutic delivery, but the structural basis of these effects is often unclear. This review groups biointerfaces by their dominant role: barrier protection, recognition, responsiveness, interaction and renewal by living cells. We compare microscopy methods for measuring coating continuity, ligand organisation, surface mechanics and changes during growth or biological transport. We also explain how artificial intelligence (AI)-assisted image analysis can convert bacterial images into reproducible single-cell measurements, using published workflows as practical examples. These methods can help connect interfacial changes with mucosal protection, immune regulation, targeted delivery and antibiofilm activity. Coating continuity, ligand organisation and mechanics are parameters associated with performance; their causal contributions remain to be established. We therefore propose combining measurements over time with matched functional assays and controlled changes to individual interface parameters. This approach can test structure-property-function relationships and guide the design of reproducible living therapeutics.

The characterisation of battery raw materials is imperative to understand the constituents that will eventually become manufactured cells, lately intensified because of the continuously evolving developments of new battery chemistries, formulations, and blends. Microscopy, also developing at a fast pace, offers an opportunity to characterise these materials, due to continuous advancements in optics, detectors, and resolution. When imaging battery raw materials, finding the optimal imaging conditions for multi-material samples is challenging; batteries are composed of hard (metallic foils, cathode and anode particles) and soft (carbon-based binder polymer) materials, as well as others, of differing chemical compositions, densities, and electron beam sensitivities. Therefore, inconsistent microscopic set ups are often required. A method to overcome these challenges is the 'sweet spot' imaging approach in a field emission scanning electron microscope (FE-SEM). Sweet spot imaging refers to the recommended conditions required to image different materials within the tested parameter space, by adjusting parameters such as the accelerating voltage (kV), the sample working distance, and the detector used. Here it is shown how adjusting these parameters optimises imaging conditions in a series of battery cathode foils, highlighting improvements in imaging particle surface features, foil structure, and binder properties, specifically at low kV and using the Inlens SE detector. Application of this approach leads to more successful segmentations for both academic and industrial research and development.

Accurate quantification of amyloid plaque pathology is essential for understanding Alzheimer's disease (AD) progression and evaluating therapeutic interventions. Yet, conventional histology relies on sampling thin tissue sections and therefore incompletely captures three-dimensional (3D) plaque distributions, introducing bias and limiting scalability. Here, we present a validated and scalable 3D analysis pipeline for whole-brain quantification of amyloid plaques in cleared mouse brain tissue imaged by light-sheet microscopy. This pipeline also integrates deep learning-based segmentation with atlas registration to enable automated, region-specific quantification across intact brain volumes. Quantitative measurements obtained using this approach showed strong agreement with gold-standard stereology, while enabling higher throughput and comprehensive volumetric assessment of amyloid plaque burden. As a demonstration of its utility in a preclinical setting, we applied the pipeline to assess the neuroprotective effects of chronic saffron supplementation in APP/PS1 mice, identifying a significant reduction in hippocampal plaque load (p < 0.01). Together, these findings establish an accessible and validated framework for scalable 3D quantification of amyloid pathology, providing a platform for neuropathological phenotyping and therapeutic evaluation in preclinical AD research.

To maximise insight from today's high throughput microscopy experiments, it is essential for a microscopist to familiarise themselves with the latest analysis software and undertake appropriate training. This can be challenging, however, due to the rapid development of software in the field, which often has complex installation steps and requires computational resources or storage beyond the capacity of personal computers. Cloud platforms running software containers remove these barriers, particularly when the platform is customised to specific end-user needs. We have developed a platform for the Volume Electron Microscopy community, Ada-vEM, that aims to lower barriers to evaluating and adopting new software for tasks such as registration and segmentation, while also providing a mechanism to streamline the provision of training. We describe the architecture of the platform and the underlying methods that enable rapid, scalable deployment of software and courses, together with its use for the delivery of remote training. We provide guidance on good practice, and hope that our experience can serve as an exemplar for other projects.

High-performance computing (HPC) has become an essential tool for BioImage analysis as recent advances in microscopy are creating increasingly large and complex datasets, the analysis of which is beyond the capabilities of personal computers. Although there is a need for wet lab scientists to utilise HPC resources, HPC training is commonly designed for scientists with prior computational experience and who are comfortable with computers, as opposed to those with limited computational experience, as is often the case with wet lab scientists. We held a 1-day workshop bringing together wet lab scientists, BioImage analysts, Bioinformaticians, and HPC engineers to help determine what motivated the wet lab scientists to learn HPC and what skills in HPC they would need in order to do their own research. Based on this workshop, we created an 8-h curriculum to teach basic HPC skills to wet lab scientists, so that they can do their own analysis without being overwhelmed with specialised technical details. This curriculum was trialled at The Francis Crick Institute to positive feedback. The curriculum framework also includes three software tracks, CellProfiler, Fiji and Python, which use familiar BioImage analysis workflows to reinforce HPC concepts. We present this curriculum here as an adaptable framework for other institutions to design a course to empower wet lab scientists to use HPC resources.